EVALUATING THE EFFECT OF BLENDING RATIO ON THE CO-GASIFICATION OF HIGH ASH COAL AND BIOMASS IN A FLUIDIZED BED GASIFIER USING MACHINE LEARNING

Öz

Co-gasification is a process that converts coal and biomass into useful products, such as syngas. Analytical and numerical approaches for modeling co-gasification process either require enormous amount of time or make a lot of assumptions which reduce consistency of the models in practical applications. Artificial Intelligence based modeling methods are used to simulate and to make predictions of outcomes of the co-gasification process. Even though previous studies result in successful modelling for specific cases, limited selection of methods and lack of implementation of cross-validation techniques causes insufficiency to explain unbiased performance evaluations and up-scale usability of the methods. In this paper, six different regression methods are employed to predict outputs of co-gasification process using a dataset containing 56 observations. Moreover, the original dataset is randomly resampled so that each model’s generalization ability is further assessed. The prediction performance of the proposed techniques on both datasets is evaluated and practical usability is discussed.

Anahtar Kelimeler

• gasification,co-gasificiation,machine learning,regression modeling,high-ash coal,biomass

Kaynakça

1. (Online) BP, (2018). Statistical Review of World Energy: https://www.bp.com/content/dam/bp/en/corporate/excel/energy-economics/statistical-review/bp-stats-review-2018-all-data.xlsx
2. N.L. Panwar, S.C. Kaushik, Surendra Kothari,Role of renewable energy sources in environmental protection: A review,Renewable and Sustainable Energy Reviews,Volume 15,Issue 3,2011,Pages 1513-1524.
3. Srivastava, Tushar. "Renewable energy (gasification)." Adv. Electron. Electr. Eng 3 (2013): 1243-1250.
4. Ali Yener Mutlu, Ozgun Yucel, An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification, Energy, Volume 165, Part A 2018, Pages: 895-901.
5. Natalia Kamińska-Pietrzak, Adam Smoliński, Selected Environmental Aspects of Gasification and Co-Gasification of Various Types of Waste,Journal
6. Akia, Mandana & Yazdani, Farshad & Motaee, Elahe & Han, Dezhi & Arandiyan, Hamid. (2014). A review on conversion of biomass to biofuel by nanocatalysts. Biofuel research journal. 1. 16-25.
7. Ud Din, Zia and Zainal, Z.A., (2016), Biomass integrated gasification–SOFC systems: Technology overview, Renewable and Sustainable Energy Reviews, 53, issue C, p. 1356-1376.
8. Karayılmazlar S, Saraçoğlu N, Çabuk Y, Kurt R (2011). Biyokütlenin Türkiye’de Enerji Üretiminde Değerlendirilmesi. Bartın Orman Fakültesi Dergisi, 13(19), 63-75.

9. Milad Arabloo, Alireza Bahadori, Mohammad M. Ghiasi, Moonyong Lee, Ali Abbas, Sohrab Zendehboudi,A novel modeling approach to optimize oxygen–steam ratios in coal gasification process,Fuel,Volume 153,2015,Pages 1-5.
10. Seo H-K, Park S, Lee J, Kim M, Chung S-W, Chung J-H, et al. Effects of operating factors in the coal gasification reaction. Korean J Chem Eng 2011;28:1851–8.
11. Kalina J. Retrofitting of municipal coal fired heating plant with integrated biomass gasification gas turbine based cogeneration block. Energy Convers Manage 2010;51:1085–92.
12. Simultaneous Steam Reforming of Tar and Steam Gasification of Char from the Pyrolysis of Potassium-Loaded Woody Biomass Tsukasa Sueyasu, Tomoyuki Oike, Aska Mori, Shinji Kudo, Koyo Norinaga, and Jun-ichiro Hayashi Energy & Fuels 2012 26 (1), 199-208.
13. Guo B, Li D, Cheng C, Lu Z, Shen Y. Simulation of biomass gasification with a hybrid neural network model. Bioresour Technol 2001;76(2).
14. Maria Puig-Arnavat, J. Alfredo Hernández, Joan Carles Bruno, Alberto Coronas, Artificial neural network models for biomass gasification in fluidized bed gasifiers, Biomass and Bioenergy,Volume 49,2013,Pages 279-289.
15. Daya Shankar Pandey, Saptarshi Das, Indranil Pan, James J. Leahy, Witold Kwapinski, Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor,Waste Management,Volume 58,2016,Pages 202-213.
16. Kohavi, Ron. (2001). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. 14.
17. Tiwary, Shishir & Ghugare, Suhas & Chavan, Prakash & Saha, Sujan & Datta, Sudipta & Sahu, Gajanan & Tambe, Sanjeev. (2018). Co-gasification of High Ash Coal–Biomass Blends in a Fluidized Bed Gasifier: Experimental Study and Computational Intelligence-Based Modeling. Waste and Biomass Valorization.
18. (Online) Tiwary, Shishir & Ghugare, Suhas & Chavan, Prakash & Saha, Sujan & Datta, Sudipta & Sahu, Gajanan & Tambe, Sanjeev. (2018). Co-gasification of High Ash Coal Biomass Blends in a Fluidized Bed Gasifier: Experimental Study and Computational Intelligence-Based Modeling. Waste and Biomass Valorization. Supplementary Material: https://static-content.springer.com/esm/art%3A10.1007%2Fs12649-018-0378-7/MediaObjects/12649_2018_378_MOESM1_ESM.docx
19. Kotsiantis, S. B., D. Kanellopoulos, and P. E. Pintelas. "Data preprocessing for supervised leaning." International Journal of Computer Science 1.2 (2006): 111-117.
20. Robert, Christian. "Machine learning, a probabilistic perspective." (2014): 62-63.
21. Takajo, Hiroaki, and Tohru Takahashi. "Noniterative method for obtaining the exact solution for the normal equation in least-squares phase estimation from the phase difference." JOSA A 5.11 (1988): 1818-1827.
22. Makarenkov, Vladimir, and Pierre Legendre. "Nonlinear redundancy analysis and canonical correspondence analysis based on polynomial regression." Ecology 83.4 (2002): 1146-1161.
23. Hawkins, Douglas M. "The problem of overfitting." Journal of chemical information and computer sciences 44.1 (2004): 1-12.
24. Prasad, Anantha M., Louis R. Iverson, and Andy Liaw. "Newer classification and regression tree techniques: bagging and random forests for ecological prediction." Ecosystems 9.2 (2006): 181-199.
25. Bai, Yuling, et al. "Short-term prediction of distribution network faults based on support vector machine." Industrial Electronics and Applications (ICIEA), 2017 12th IEEE Conference on. IEEE, 2017.
26. Lu, Chi-Jie, Tian-Shyug Lee, and Chih-Chou Chiu. "Financial time series forecasting using independent component analysis and support vector regression." Decision Support Systems 47.2 (2009): 115-125.
27. Scholkopf, Bernhard, and Alexander J. Smola. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2001.
28. Zhang, Guoqiang, B. Eddy Patuwo, and Michael Y. Hu. "Forecasting with artificial neural networks:: The state of the art." International journal of forecasting 14.1 (1998): 35-62.
29. Venayagamoorthy, Ganesh K., and Venu Gopal Gudise. "Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks." (2003).
30. Kohavi, Ron. "A study of cross-validation and bootstrap for accuracy estimation and model selection." Ijcai. Vol. 14. No. 2. 1995.
31. Ambroise, Christophe, and Geoffrey J. McLachlan. "Selection bias in gene extraction on the basis of microarray gene-expression data." Proceedings of the national academy of sciences 99.10 (2002): 6562 6566.
32. QUINLAN, John R., et al. Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence. 1992. p. 343-348.